Machine Learning, Deep Learning & AI Platforms

Machine Learning, Deep Learning & AI Platforms

As enterprises mature in their appreciation and use of machine learning, deep learning, and AI, a critical question arises: How can they scale and industrialize ML development?

Part of the answer to this question is supporting data scientists and ML engineers with appropriate processes and technology platforms.

To help enterprises understand the platform landscape and attendant issues, TWiML is excited to present a series of eBooks and podcasts on enterprise ML & AI platforms.

Kubernetes for Machine Learning, Deep Learning & AI

In the first book in the series we look at enterprise ML and AI platform needs from the bottom up, with a focus on infrastructure support for data science and machine learning. We believe Kubernetes is a strong contender for ML/DL workloads, and have focused the first book on this topic.

Agile Machine Learning Platforms

In the second book, we explore these same needs from the top down, starting from the platforms that leading data first companies have built, such as Facebook, Uber, and Google, the process disciplines that they embody, and what these say about the shape of emerging Agile Machine Learning Platforms for the enterprise.

Podcasts

Stay tuned for our extensive podcast coverage of this topic throughout the month. We’ll be featuring interviews with data science, machine learning and infrastructure innovators from Facebook, Airbnb, Linkedin, OpenAI, Comcast, Shell and more. Check out the series page for links to the individual shows, or you can sign up to be notified.